As serverless architectures become more popular, customers need a framework of patterns to help them deploy their workloads without managing servers or operating systems. This session will discuss several re-usable serverless patterns such as web apps, data processing, data lake, and more.

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G O T O
Amsterdam 2018
Serverless Architectural Patterns
Boaz Ziniman, Technical Evangelist – Amazon Web Service
@ziniman
boaz.ziniman.aws

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Serverless Beyond Lambda

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Pattern 1:
Web App/Microservice/API

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Web application
Data stored in
Amazon
DynamoDB
Dynamic content
in AWS Lambda
Amazon API
Gateway
Browser
Amazon
CloudFront
Amazon
S3
Amazon Cognito

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Amazon API
Gateway AWS
Lambda
Amazon
DynamoDB
Amazon
S3
Amazon
CloudFront
• Bucket Policies
• ACLs
• Origin Access Identity (OAI)
• Geo-Restriction
• Signed Cookies
• Signed URLs
• DDOS Protection
IAM IAM
Serverless web app security
• Throttling
• Caching
• Usage Plans
• ACM
Static Content
Browser
Amazon Cognito

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Custom
Authorizer
Lambda
functionClient
Lambda
function
Amazon API
Gateway
Amazon
DynamoDB
AWS Identity &
Access Management
SAML
Two types:
• TOKEN - authorization token
passed in a header
• REQUEST – all headers, query
strings, paths, stage variables or
context variables.Custom
Authorizer
Cognito
User Pool
Custom Authorizers

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Multi-Region with API Gateway
us-west-2
us-east-1
Client
Amazon
Route 53
Regional
API
Endpoint
Regional
API
Endpoint
Custom
Domain
Name
Custom
Domain
Name
API Gateway
API Gateway
Lambda
Lambda
api.mycorp.com
CNAME
CNAM
E

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http://bit.ly/ServerlessShop
https://github.com/patrick-michelberger/serverless-shop

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Pattern 2:
Stream Processing

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Stream processing characteristics
• High ingest rate
• Near real-time processing (low latency from ingest to
process)
• Spiky traffic (lots of devices with intermittent network
connections)
• Message durability
• Message ordering

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Streaming data ingestion
Amazon CloudWatch:
Delivery metrics
Amazon S3:
Buffered files
Kinesis
Agent
Record
Producers Amazon Redshift:
Table loads
Amazon Elasticsearch Service:
Domain loads
Amazon S3:
Source record backup
AWS Lambda:
Transformations &
enrichment
Amazon DynamoDB:
Lookup tables
Raw records
Lookup
Transformed records
Transformed recordsRaw records
Amazon Kinesis Firehose:
Delivery stream

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Best practices
• Tune Firehose buffer size and buffer interval
• Larger objects = fewer Lambda invocations, fewer S3 PUTs
• Enable compression to reduce storage costs
• Enable Source Record Backup for transformations
• Recover from transformation errors
• Follow Amazon Redshift Best Practices for Loading Data
• How to handle time series, sorted data

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bit.ly/AWSIoTColours

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Colour cube voting
Amazon
S3 Bucket
Amazon
CloudFront
AWS IoT
Amazon
Cognito
Amazon
Cognito

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Colour cube voting
Amazon
S3 Bucket
Amazon
CloudFront
AWS IoT
Amazon
Cognito
Amazon
Cognito
Amazon
Elasticsearch
Amazon
Kinesis
Firehose

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Real-time analytics
Amazon Kinesis Streams:
Ingest stream
Amazon Kinesis Analytics:
Time window aggregation
Amazon Kinesis Streams:
Aggregates stream
Amazon Kinesis Firehose:
Error stream
Amazon S3:
Error records
Record
Producers
AWS Lambda:
Alert function
Amazon DynamoDB:
Device thresholds
AWS SNS:
Notifications

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Amazon Kinesis Streams and AWS Lambda
• Number of Amazon Kinesis Streams shards corresponds to concurrent invocations
of Lambda function
• Batch size sets maximum number of records per Lambda function invocation
Amazon Kinesis:
Stream
AWS Lambda:
Processor function
Streaming source Other AWS services

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Fan-out pattern
Fan-out pattern trades strict message ordering vs higher throughput & lower latency
Amazon Kinesis:
Stream
Lambda:
Dispatcher function
Lambda:
Processor function
Increase throughput, reduce processing latency
Streaming source

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Thomson Reuters – Product Insight
Solution for usage analysis tracking:
Capture, analyze, and visualize analytics data generated by offerings, providing
insights to help product teams continuously improve the user experience
Throughput: Tested 4,000 requests / second
Growing to 10,000 requests / second or 25 Billion requests / month
Latency: new events to user dashboards in less than 10 seconds
Durable: no data loss since inception

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Pattern 3:
Operations Automation

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Automation characteristics
• Periodic jobs
• Event triggered workflows
• Enforce security policies
• Audit and notification
• Respond to alarms
• Extend AWS functionality
… All while being Highly Available, Scalable and Auditable

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AWS Ops Automator
Amazon CloudWatch:
Time-based events
AWS Lambda:
Event handler
AWS Lambda:
Task executors
AWS SNS:
Error and warning notifications
Resources in multiple AWS
Regions and Accounts
Amazon EC2 Instances
Tags
OpsAutomatorTaskList CreateSnapshotAmazon DynamoDB:
Task configuration & tracking
Amazon CloudWatch:
Logs
Amazon Redshift Clusters
https://aws.amazon.com/answers/infrastructure-management/ops-automator/

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Image recognition and processing
Web App
Amazon DynamoDB:
Image meta-data & tags
Amazon Cognito:
User authentication
Amazon S3:
Image uploads
AWS Step Functions:
Workflow orchestration
Start state machine execution
1
Extract image meta-data
2
Amazon Rekognition:
Object detection
Invoke Amazon Rekognition
Generate image thumbnail
3
3Store meta-data and tags
4
https://github.com/awslabs/lambda-refarch-imagerecognition

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Step Functions state machine

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Enforce security policies
RDP from
0.0.0.0/0
RDP from
0.0.0.0/0
CloudWatch Event Bus in
another AWS Account
New Security Group ingress rule Amazon CloudWatch Events:
Rule
AWS Lambda:
Remediate and alert
AWS SNS:
Email alert
Ingress rule deleted

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Autodesk - Tailor
Serverless AWS Account Provisioning and Management Service:
• Automates AWS Account creation,
• Configures IAM, CloudTrail, AWS Config, Direct Connect, and VPC
• Enforces corporate standards
• Audit for compliance
Provisions new Accounts in 10 minutes vs 10 hours in earlier manual process
Open source and extensible: https://github.com/alanwill/aws-tailor

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Best practices
• Gracefully handle API throttling by retrying with an exponential back-off
algorithm (AWS SDKs do this for you)
• Publish custom metrics from your Lambda function that are meaningful for
operations (e.g. number of EBS volumes snapshotted)
• Enable X-Ray tracing for your Lambda functions
• Document how to disable event triggers for your automation when
troubleshooting

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Additional Patterns

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Serverless Data Lake
S3
Bucket(s)
Key
Management
Service
Amazon
Athena
AWS
CloudTrail
Amazon
Cognito
AWS IAM
Amazon
Kinesis
Streams
Amazon
Kinesis
Firehose
Amazon ES
Amazon
QuickSight
AWS Glue
Amazon
DynamoDB
Amazon
Macie
Amazon API
Gateway
AWS IAM
Amazon
Redshift
Spectrum
AWS
Direct
Connect
Ingest
Catalog & Search
Security & Auditing
API/UI
Analytics & Processing
AWS Glue
AWS
Lambda

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Search and Data Catalog
• DynamoDB as
Metadata repository
• Amazon Elasticsearch
Catalog & Search
AWS Lambda
AWS Lambda
Metadata Index
(DynamoDB)
Search Index
(Amazon ES)
ObjectCreated
ObjectDeleted PutItem
Update Stream
Update Index
Extract Search Fields
S3 Bucket
https://aws.amazon.com/answers/big-data/data-lake-solution/

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Further Reading
• Optimizing Enterprise Economics with Serverless Architectures
https://d0.awsstatic.com/whitepapers/optimizing-enterprise-economics-serverless-architectures.pdf
• Serverless Architectures with AWS Lambda
https://d1.awsstatic.com/whitepapers/serverless-architectures-with-aws-lambda.pdf
• Serverless Applications Lens - AWS Well-Architected Framework
https://d1.awsstatic.com/whitepapers/architecture/AWS-Serverless-Applications-Lens.pdf
• Streaming Data Solutions on AWS with Amazon Kinesis
https://d1.awsstatic.com/whitepapers/whitepaper-streaming-data-solutions-on-aws-with-amazon-kinesis.pdf
• AWS Serverless Multi-Tier Architectures
https://d1.awsstatic.com/whitepapers/AWS_Serverless_Multi-Tier_Archiectures.pdf

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Summary
Use DevOps tools to automate your serverless deployments
Apply serverless patterns for common use-cases:
• Web application
• Data Lake Foundation
• Stream processing
• Operations automation
What will you build with Serverless?

34. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Boaz Ziniman – Technical Evangelist, AWS
@ziniman
boaz.ziniman.aws